The present relates in general to processing detector information in a pulse oximetry system and, in particular, to the determination of differential values for use in blood oxygenation calculations with reduced noise sensitivity.
In the field of photoplethysmography, light signals corresponding with two or more different centered wavelengths may be employed to non-invasively determine various blood analyte concentrations. By way of example, blood oxygen saturation (SpO2) levels of a patient""s arterial blood are monitored in pulse oximeters by measuring the absorption of oxyhemoglobin and reduced hemoglobin using red and infrared light signals. The measured absorption data allows for the calculation of the relative concentrations of reduced hemoglobin and oxyhemoglobin, and therefore SpO2 levels, since reduced hemoglobin absorbs more light than oxyhemoglobin in the red band and oxyhemoglobin absorbs more light than reduced hemoglobin in the infrared band, and since the absorption relationship of the two analytes in the red and infrared bands is known.
To obtain absorption data, pulse oximeters comprise a probe that is releasably attached to a patient""s appendage (e.g., finger, ear lobe or the nasal septum). The probe directs red and infrared light signals to the appendage, or tissue-under-test. The light signals are provided by one or more sources which are typically disposed in the probe. A portion of the light signals is absorbed by the tissue-under-test and the intensity of the light transmitted through or reflected by the tissue-under-test is detected, usually by at least one detector that may also be located in the probe. The intensity of an output signal from the detector(s) is utilized to compute SPO2 levels, most typically via a processor located in a patient monitor interconnected to the probe.
As will be appreciated, pulse oximeters rely on the time-varying absorption of light by a tissue-under-test as it is supplied with pulsating arterial blood. The tissue-under-test may contain a number of non-pulsatile light absorbers, including capillary and venous blood, as well as muscle, connective tissue and bone. Consequently, detector output signals typically contain a large non-pulsatile, or DC, component, and a relatively small pulsatile, or AC, component. It is the small pulsatile, AC component that provides the time-varying absorption information utilized to compute arterial SpO2 levels.
In this regard, the red and infrared signal portions of pulse oximeter detector output signals each comprise corresponding large DC and relatively small AC components. The red and infrared signal portions have an exponential relationship to their respective incident intensities at the detector(s). As such, the argument of the red and infrared signal portions have a linear relationship and such portions can be filtered and processed to obtain a ratio of processed red and infrared signal components (e.g., comprising their corresponding AC and DC components), from which the concentration of oxyhemoglobin and reduced hemoglobin in the arterial blood may be determined. See, e.g., U.S. Pat. No. 5,934,277. By utilizing additional light signals at different corresponding centered wavelengths it is also known that carboxyhemoglobin and methemoglobin concentrations can be determined. See, e.g., U.S. Pat. No. 5,842,979.
As noted, the pulsatile, AC component of a pulse oximeter detector output signal is relatively small compared to the non-pulsatile DC component. Consequently, the accuracy of analyte measurements can be severely impacted by small amounts of noise. One such type of noise relates to effects on the measured absorption data as a result of undesired variations in the path length of light signals as they pass through the tissue-under-test. Such variations are most typically caused by patient movement of the appendage to which a pulse oximetry probe is attached.
A number of different approaches have been utilized to reduce the deleterious effects of patient motion in pulse oximeters. For example, pulse oximeter probes have been developed to enhance the physical interface between the probe and tissue-under-test, including the development of various clamp type probe configurations and secure wrap-type probe configurations. Further, numerous approaches have been developed for addressing motion contaminated data through data processing techniques. While such processing techniques have achieved a degree of success, they often entail extensive signal processing requirements, thereby contributing to increased device complexity and componentry costs.
Other types of noise include electrical and optical phenomena that cause artifact in the pulsatile component of the measured absorption data. For example, effects due to ambient light and interfering electrical signals can provide significant noise components. Many of these noise sources are not easily filtered out of the detector signal and, therefore, are reflected in the measured absorption data. It will be appreciated that such noise in the measured absorption data can significantly affect blood oxygen saturation calculations if not adequately accounted for in signal/data processing.
The case of calculating derivatives of the measured absorption data signal is illustrative. As noted above, pulse oximetry blood oxygenation calculations are generally based on measuring the relative time varying absorption or optical signal attenuation at two or more wavelengths by the tissue-under-test. Specifically, a ratio of corresponding differential values, such as the normalized derivative of attenuation (NdA) for each of two centered wavelengths or channels may be calculated. The time derivative of the attenuation divided by the attenuation provides the NdA. The ratio of the NdAs for the red and infrared wavelengths, as often employed in pulse oximeters, is directly proportional to SpO2.
The NdAs for each wavelength have generally been calculated in two ways. Most commonly, the NdAs have been approximated by measuring a peak to trough amplitude of the pulsatile signal. However, this methodology is sensitive to noise at the data points associated with the peak and trough. Moreover, the response time of such pulse oximeters is limited due to the elongated sampling interval required for NdA calculations. Additionally, this methodology can suffer from reduced accuracy if the delays of the high pass AC filters and low pass DC filters, used to separate the pulsatile and non-pulsatile components of the detector signal in connection with peak and trough identification, are not carefully matched. As noted above, calculation of the NdAs involves dividing the time derivative of the attenuation by the overall attenuation including the DC component. This calculation assumes that the time derivative and DC component are sampled at substantially the same time and, accordingly, any differences in the filter delays can introduce an element of error. Additionally, such filters can take considerable time to stabilize before the oximeter can calculate accurate derivatives.
Another common method of estimating the NdA involves calculating a difference between successive data points of the processed detector signal. This difference is normalized by dividing by an average DC value for the two data points. This methodology avoids many of the disadvantages of peak-to-trough calculations, and generates output for every sample, but the change in signal level is much smaller as between successive samples as compared to peak-to-trough amplitude calculations. As a result, individual measurements are sensitive to noise.
In pulse oximetry systems developed by Datex-Ohmeda, Inc., successive data point calculations are employed but multiple data sets are utilized to determine the NdA ratio used for Sp02 calculations thereby improving accuracy. In particular, absorption related values are calculated for each channel for multiple samples over a measurement period. For each sample time during the measurement period, a ratio of the corresponding absorption related values for the channels, e.g., red and infrared, is calculated, thereby yielding a set of ratios. This may be visualized as a graph plotting a number of points as red absorption related values against infrared absorption related values. Ideally, these points define a line and the slope of the line is proportional to Sp02. The slope of the line may be determined, for example, by a linear regression analysis.
The noted Datex-Ohmeda system thus involves two separate processes. First, differential values are calculated using data points of a single channel. Second, a differential analysis is performed on the set of channel-by-channel ratios or graphical data points to determine a slope value. The latter process may involve a linear regression analysis. The former process has been limited to consideration of successive data points.
The present invention is directed to processing of measured absorption data to obtain differential values, such as NdAs, with reduced noise sensitivity. The invention allows for provision of output at frequent intervals, such as on a sample-by-sample basis, while mitigating noise sensitivity associated with calculating differentials based solely on successive samples e.g., successive measured absorption data points. In order to facilitate accurate detection of noise such as for motion correction, the present invention further allows for separate storage of and access to pre-processed and post-processed data sets, such that post-processed data can be used in analyte calculation algorithms and preprocessed data can be used for making motion or other noise correction calculations.
In accordance with one aspect of the present invention, differential values are determined using a window including non-successive samples, thereby reducing noise sensitivity associated with the sometimes small differential values of successive samples. As noted above, successive sample calculations have certain advantages over peak-to-trough calculations including the ability to generate output for each sample, but are sensitive to noise due to small differential values. A process in accordance with the present aspect of the invention uses windows including non-successive values by: receiving a series of data samples for a single centered wavelength corresponding to a measurement period; defining a moving sampling window that has a time dimension less than the measurement period; accessing first and second data samples within the sampling window for a given sampling interval, where the first and second data samples are separated by an intervening data sample; using the first and second data""samples to calculate a differential value; and using the differential value in determining the parameter value related to blood oxygen saturation. The determination of a differential value as reflected in this process may be applied to each of multiple channels, e.g., a red centered wavelength and infrared centered wavelength, to enable channel ratio based SpO2 calculations. This process advantageously allows for outputs on a per sample basis while avoiding the small differentials of successive sample differential calculations.
In accordance with another aspect of the present invention, differential values are determined using more than two samples. The associated process involves: receiving a series of data samples for a single-centered wavelength corresponding to a measurement; accessing first, second and third data samples of the series of data samples; using the first, second and third data samples to calculate a differential value; and using the differential value in determining the parameter value related to blood oxygen saturation. Preferably, multiple values, obtained over a portion of the pulsatile signal waveform are used to calculate a differential value. The optimal number of points depends in part on the sampling rate of the oximetry application. In a preferred implementation, samples are taken over a window of about 0.1 to 0.5 seconds, and, more preferably, about 0.25 to 0.33 seconds. For a sampling rate of 30 samples per second, this corresponds to about 3 to 15 and more preferably, about 7-10 samples per window. The use of multiple samples in this manner to determine a differential value allows for improved statistical analysis to reduce the effect of noise.
In a preferred implementation, a best-fit function analysis such as a linear regression analysis is performed on the multiple data points to compute a differential value. In this regard, a large number of points can be used to maximize noise rejection. It has been found, however, that such a best-fit function analysis, in addition to smoothing out noise effects, can undesirably smooth out abrupt signal changes corresponding to useful physiological information. Thus, in accordance with a further aspect of the present invention, certain points in the window of data points under analysis are emphasized in processing so that the best-fit function analysis can use a large number of points to maximize noise rejection while minimizing the reduction in differential amplitude (smoothing) associated with abrupt signal changes, e.g., due to high heart rates. In this regard, the best-fit function analysis may be conducted relative to a moving window of data points centered about a nominal instantaneous time interval for which the differential is to be calculated. Weighting selected center data points more heavily in the best-fit function analysis allows for noise reduction while reducing undesired smoothing. Such weighting may be accomplished by applying a suitable finite impulse response (FIR) filter window function to the windowed data. Examples include box, triangle, Gaussian and Blackman filter windows.
It will be appreciated that such processing yields a data set with reduced noise effects. This processed data set allows for increased accuracy in the blood oxygen saturation calculations. However, it has also been found advantageous to analyze the preprocessed, measured absorption data in order to identify noise levels associated with a particular data sets. For example, the preprocessed measured absorption data may be analyzed to identify a noise level associated with patient motion. Such a noise level may be used to exclude particular data sets from blood oxygen saturation calculations and/or to de-emphasize or otherwise compensate for motion effects. It will be appreciated that access to preprocessed measured absorption data may be preferred for such noise analyses.
In accordance with yet another aspect of the present invention, both preprocessed and postprocessed data are used in making a blood oxygen saturation determination. The associated method includes the steps of: storing a first set of preprocessed data based on a measured absorption signal; processing the first set of data to remove artifact therefrom and generate a second set of postprocessed data; accessing the first set of preprocessed data to calculate a first value for use in a blood oxygen saturation determination; accessing the second set of postprocessed data to determine a second value for use in a blood oxygen saturation determination; and using the first value and the second value to calculate a parameter value related to blood oxygen saturation. The process for removing artifact from the preprocessed data set may involve a differential value calculation as described above for rejecting noise. In such a case, the first value may be a differential value such as an NDA. The second value may be, for example, a noise threshold value for use in rejecting certain sample sets or an empirically derived correction factor to correct for motion artifact.